AI in Oncology: Transforming Cancer Detection through Machine Learning and Deep Learning Applications

Alishba Nadeem; Chengjuan Zhang; Faisal Mehmood; Kangdongs Liu; Muhammad Aftab; Yanan Jiang; Zigang Dong

REVIEW 4 major objections 5 minor 1 cited by

AI in Oncology: Transforming Cancer Detection through Machine Learning and Deep Learning Applications

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper reviews evidence across ten cancer types and argues that machine-learning and deep-learning systems can match or exceed human diagnostic performance, improving early detection and enabling more personalized treatment.

desk verdict A plausible, broad AI-in-oncology review undone by sloppy number transcription and no stated method; the conclusion is likely right, but the evidence as presented cannot be trusted. read the letter →

arxiv 2501.15489 v1 pith:TOVBMEVX submitted 2025-01-26 cs.AI eess.IVq-bio.QM

Muhammad Aftab , Faisal Mehmood , Chengjuan Zhang , Alishba Nadeem , Zigang Dong , Yanan Jiang , Kangdongs Liu This is my paper · ORCID

classification cs.AIeess.IVq-bio.QM

keywords artificialintelligencemachinelearningdeepcancerdiagnosismedicalimagingoncologycomputer-aideddetectionprecisionmedicine

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper is a review of artificial intelligence in oncology, covering ten major cancer types. It argues that machine-learning and deep-learning systems, applied to medical imaging, genomic sequencing, and pathology slides, can detect cancers earlier, improve diagnostic accuracy, cut false positives and negatives, and help tailor treatment per patient. The authors' claim matters because if the reported results transfer to clinical practice, AI could relieve specialist shortages, lower screening costs, and extend reliable cancer diagnostics to underserved regions. The review's case rests on the performance statistics of the cited studies, which it presents as evidence that AI now matches or exceeds human experts across many diagnostic tasks.

What carries the argument

The central object is the deep-learning pipeline over medical data, with convolutional neural networks (CNNs) as the workhorse for images from CT, MRI, ultrasound, endoscopy, and histopathology slides, and machine-learning classifiers for multi-omics data such as cfDNA, RNA-seq, DNA methylation, and copy-number profiles. Named architectures include ResNet, VGG, Inception, U-Net, Mask R-CNN, YOLO, and ensemble or CAD/CADe systems. These models carry the argument because each cited study's reported performance is the evidence the review marshals for AI's clinical value.

What would settle it

A systematic audit of the reported metrics would settle it: for instance, the MammoScreen result in Section 1.1 reports an AUC of 0.7494 with a 95% confidence interval of 0.754–0.840, which is internally inconsistent because the point estimate lies outside its own interval; a re-run of the cited analyses, or a meta-analysis of prospective AI-assisted screening trials that failed to show improved sensitivity and specificity, would refute the review's central claim.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that AI-based diagnostic pipelines have reached a point where they consistently perform at or above the level of human specialists across a broad span of oncology tasks: detecting small lung nodules on CT, interpreting mammograms, classifying colorectal polyps during colonoscopy, diagnosing early esophageal and gastric cancers from endoscopic images, and grading prostate biopsies and skin lesions. The review documents this through tables of reported accuracy, AUC, sensitivity, and specificity values, and it argues that the pattern across ten cancers constitutes evidence for a general claim: AI can improve early detection, reduce diagnostic error, and support precision oncology.

Load-bearing premise

The review's conclusions rest on the assumption that the performance numbers reported in the cited studies are accurate, correctly transcribed, and comparable across datasets; if those numbers are wrong or taken out of context, the review's claims inherit the errors.

Editorial extensions

If this is right

If the reported performance holds in prospective trials, AI-assisted screening can lower the number of false-positive and false-negative findings while easing the workload of radiologists and pathologists.
Multi-omics AI models can be used to identify cancer subtypes and predict which patients are more likely to respond to immunotherapy, enabling more personalized treatment decisions.
Real-time AI systems during endoscopy can raise adenoma detection rates and reduce missed lesions, which could lower colorectal cancer incidence.
AI tools that match expert readers may bring reliable cancer screening to regions that lack enough specialized clinicians.
The same imaging-plus-genomics pipeline applies across tumor types, so advances in one cancer can be adapted to others.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A reasonable extension beyond the paper is that the biggest near-term impact of AI cancer detection may be in low-resource settings, where AI assistance could substitute for scarce specialists, provided the models are trained on representative local data.
The cross-cancer similarity in methods suggests that transfer learning and foundation models trained on data-rich cancers (lung, breast) could be adapted to rare cancers where large labeled datasets do not exist.
The internal inconsistency in the MammoScreen AUC and confidence interval in Section 1.1 hints that some transcribed performance numbers may be unreliable; standardizing how metrics are reported would strengthen the evidence base the review depends on.
The review's reliance on retrospective and single-center studies implies that the real test is prospective, multi-center validation under deployment conditions.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague,

This is a narrative review of AI in oncology spanning ten cancer types. There is no new data, no method, no framework; the contribution is a themed list of citations and tables of performance metrics. It does organize a lot of literature, and a newcomer could get a quick map of the field from the scope alone.

But the evidence base is not trustworthy as it stands. Section 1.1 reports MammoScreen AUC 0.7494 with a 95% CI of 0.754–0.840, which is internally impossible: the lower bound exceeds the point estimate. Table 4 gives sensitivity as "90 to 47%" instead of 90.47%. Figure 11's caption is about prostate cancer while the figure sits in the cervical cancer section. These are not cosmetic typos; they are warning signs that the transcription from primary sources is unreliable. This is a review, so faithful reporting is the entire value proposition.

There is also no stated methodology: no search strategy, no inclusion criteria, no critical appraisal. The paper aggregates numbers from heterogeneous datasets and designs and compares them as if they were apples to apples. That is a real limitation for a review aimed at clinicians.

What is genuinely useful is the breadth. The tables compile many studies in one place, and if the numbers were verified against primary sources, the paper could serve as a reference list. The central claim—that AI improves detection across multiple cancer types—is consistent with the wider literature, so the conclusion is not wrong; it just is not adequately supported by the paper's own evidence.

If this crossed my desk, I would not send it to peer review in its current form. Any referee would trip over the MammoScreen CI in the first five minutes. I would send it back to the authors with a demand to re-check every metric against the primary sources, add a stated search and inclusion protocol, and replace the cheerleading with critical appraisal. If they fixed those things, it might deserve referee time as a review.

Overall: a plausible takeaway and some organizing value, but currently too sloppy to cite or trust. You would get more from a well-conducted systematic review on any single cancer type.

Referee Report

4 major / 5 minor

Summary. This manuscript is a narrative review of artificial intelligence (AI), machine learning (ML), and deep learning (DL) applications in oncology, covering lung, breast, colorectal, liver, gastric, esophageal, cervical, thyroid, prostate, and skin cancers. For each cancer type it summarizes conventional diagnostic methods, their limitations, and recent AI-based approaches, with performance metrics compiled into tables. The paper argues that AI can improve early detection, diagnostic accuracy, and personalized treatment planning, and it concludes that AI is poised to transform oncology while acknowledging challenges such as data quality, algorithm bias, and regulatory/ethical concerns. The review contains no original models or derivations; its evidentiary value depends entirely on faithful transcription of the cited primary studies.

Significance. If the transcribed performance figures are accurate, the review would be a useful, broad reference snapshot of the AI-in-oncology literature, and it would support a measured conclusion that AI-assisted tools can augment diagnostic workflows across multiple cancer types. The manuscript also has strengths: it covers a wide range of cancer types in a structured way, includes many tables of cited studies and performance metrics, and explicitly flags clinically important caveats such as the lack of training data for darker skin tones, the heterogeneity of datasets, and the need for interpretability and clinical validation. However, the synthesis is only as reliable as its source transcription, and several verified internal inconsistencies currently undermine confidence in the numerical evidence base. Because the paper is a review, these errors are correctable, but they must be addressed before the central claim can be accepted.

major comments (4)

[Section 1.1] The reported MammoScreen AUC of 0.7494 with a 95% CI of 0.754 to 0.840 is internally impossible, since the lower bound of a confidence interval must be below the point estimate. This is not cosmetic: the paragraph uses this result to claim that adding AI improves breast cancer screening accuracy, yet the printed numbers actually make the AI-assisted AUC appear lower than the unassisted AUC of 0.769 (0.724–0.814). Please recheck the values against reference [30] and correct the point estimate or the confidence interval, and audit the other figures in the same paragraph (false-positive and false-negative reductions) for consistency with the source.
[Table 4] In the Fuzzy C-Means (FCM) row, the performance is printed as “Sn value is 90 to 47% Sp value is 84 to 84%”, which is not a valid reporting of sensitivity and specificity. The body text in Section 3.1 states the correct values as 90.47% and 84.84%, so the table entry appears to be a garbled transcription. As printed, the table cannot support the claim that FCM achieved 87% accuracy with these sensitivity and specificity values. Please correct the table formatting and verify the underlying numbers against reference [167].
[Tables 10, 12, and 26] Beyond the two errors above, several tables contain entries that appear to be out of place, malformed, or anachronistic. In Table 10, the “Function” entries for “Exosomal miRNAs in Liver Injury” and “Bibliometric Analysis on AI in Liver Cancer” appear to be swapped with the neighboring rows. In Table 12, the entry “MICCAI 2027 LITS database” is presumably “MICCAI 2017 LiTS database,” and Table 26 contains many rows with missing or blank performance cells and at least one “VGG-16” row with “-” as the dataset. Since the review’s conclusions rest on the accuracy of the cited metric tables, please perform a systematic source-verification pass rather than fixing only the isolated typos.
[Abstract and Section 13] The abstract and conclusion state that AI leads to “substantial improvements in patient outcomes,” but the cited evidence in this review is predominantly surrogate metrics such as AUC, sensitivity, and specificity. The breast cancer section (Section 3.1) itself acknowledges a lack of randomized controlled studies directly comparing AI as an independent screening system with radiologist interpretation, and the one screening example in Section 1.1 contains the internal inconsistency noted above. Please temper the conclusion to say that AI has shown promising improvements in diagnostic accuracy in retrospective and prospective cohorts, and explicitly note that direct evidence of improved patient-level outcomes remains limited.

minor comments (5)

[Section 8.4 and Figure 11] Figure 11 is captioned “Artificial Intelligence Workflow in Prostate Cancer Diagnosis and Management” but appears in the cervical cancer section and is referenced there; please either replace the figure with a cervical-cancer-specific diagram or move it to the prostate cancer section with matching text.
[Section 10.3 and Section 11] Figure 13 is referenced twice: once for prostate cancer in Section 10.3 and once for skin cancer in Section 11. The skin-cancer reference should be to Figure 14, since Figure 13 depicts prostate cancer. Please renumber or re-reference the figures consistently.
[Section 5.3.1 and Table 12] The text in Section 5.3.1 refers to “colon cancer spreading to the liver” in a liver cancer section; this should be “colorectal cancer” for precision. Similarly, Table 12’s “MICCAI 2027” should be corrected to the actual year of the dataset challenge.
[General] The manuscript contains numerous typos and inconsistent formatting, examples including “color ectal cancer” (Section 4), “faces” for “feces” (Section 4.1), “Al” for “AI” (Table 1 caption area), and “a motel YOLO3” (Table 28). A careful copyedit is needed throughout.
[Section 1.1] The paragraph after the MammoScreen discussion reports US/UK false-positive and false-negative reductions and cites reference [29], but the preceding sentence about “500 randomly selected cases” appears to describe a different study; please make the narrative attribution to specific sources explicit for each set of numbers.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular reasoning: the paper is a narrative literature review that derives no quantitative predictions from its own inputs.

full rationale

The paper is a narrative review of AI applications in oncology. It contains no original model, no fitted parameters, no derivation chain, and no equation that is defined in terms of its own output. Its claims are supported by citations to external studies, and the review does not construct any prediction from the data it summarizes. The internally inconsistent MammoScreen AUC/CI pair in Section 1.1 (AUC = 0.7494 vs. 95% CI 0.754–0.840) and the malformed sensitivity/specificity entries in Table 4 are transcription or reporting errors that undermine the reliability of the evidence base, but they are not circularity: the review is not assuming its own conclusion, and no step reduces to its own input by construction. Similarly, the paper's reliance on cited benchmarks is external evidence, not self-citation, and no load-bearing argument is justified by a citation to the authors' own prior work. The central claim—that AI can improve cancer detection and diagnosis—is a synthesis of external findings, so a circularity score of 0 is the appropriate finding.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The central claim of the review depends on two unverified assumptions: (1) the cited studies' numeric results are accurate and faithfully transcribed, and (2) the selected studies and cancer types are representative of the AI-in-oncology field. The paper itself supplies no independent validation, code, or alternative analysis.

assumptions (2)

domain assumption Reported performance metrics in cited studies are accurate and internally consistent.
The review's tables and text rest on numbers drawn from many papers; internal contradictions (e.g., Table 4, MammoScreen AUC) show this assumption is unsafe.
domain assumption The selected studies are representative of the broader literature on AI in oncology.
No inclusion or exclusion criteria or search strategy is described, so representativeness is simply assumed.

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Cite this review

Pith. "Pith review of AI in Oncology: Transforming Cancer Detection through Machine Learning and Deep Learning Applications." pith.science (2026). https://pith.science/paper/TOVBMEVX

@misc{pith2026250115489,
  author       = {Pith},
  title        = {Pith review of: AI in Oncology: Transforming Cancer Detection through Machine Learning and Deep Learning Applications},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TOVBMEVX}},
  note         = {Machine review of arXiv:2501.15489}
}

read the original abstract

Artificial intelligence (AI) has potential to revolutionize the field of oncology by enhancing the precision of cancer diagnosis, optimizing treatment strategies, and personalizing therapies for a variety of cancers. This review examines the limitations of conventional diagnostic techniques and explores the transformative role of AI in diagnosing and treating cancers such as lung, breast, colorectal, liver, stomach, esophageal, cervical, thyroid, prostate, and skin cancers. The primary objective of this paper is to highlight the significant advancements that AI algorithms have brought to oncology within the medical industry. By enabling early cancer detection, improving diagnostic accuracy, and facilitating targeted treatment delivery, AI contributes to substantial improvements in patient outcomes. The integration of AI in medical imaging, genomic analysis, and pathology enhances diagnostic precision and introduces a novel, less invasive approach to cancer screening. This not only boosts the effectiveness of medical facilities but also reduces operational costs. The study delves into the application of AI in radiomics for detailed cancer characterization, predictive analytics for identifying associated risks, and the development of algorithm-driven robots for immediate diagnosis. Furthermore, it investigates the impact of AI on addressing healthcare challenges, particularly in underserved and remote regions. The overarching goal of this platform is to support the development of expert recommendations and to provide universal, efficient diagnostic procedures. By reviewing existing research and clinical studies, this paper underscores the pivotal role of AI in improving the overall cancer care system. It emphasizes how AI-enabled systems can enhance clinical decision-making and expand treatment options, thereby underscoring the importance of AI in advancing precision oncology

Figures

Figures reproduced from arXiv: 2501.15489 by the authors.

**Figure 1.** This image highlights AI's transformative role in detecting and diagnosing ten types of cancer, enabling precise and rapid analysis across various cancers. An examination was conducted on MammoScreen system to demonstrate the benefits of integrating novel AI technology with radiologists. This approach facilitates the detection of suspicious areas in 2D mammograms indicative of potential breast cancer presence and en… view at source ↗

**Figure 2.** Applications of AI, ML, and DL in oncology and digital health care can predict the best line of treatment and cure health issues. The application of AI, ML and DL are shown in [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** The image shows AI-based machine for analyzing multi-omics cancer patients' and healthy donors' data to improve cancer diagnosis and treatment. By integrating genomics, transcriptomics, and proteomics data, the AI algorithm achieves several key outcomes: meeting new challenges related to identification of cancers subtypes, predicting diseases progression and survival probabilities and discovering new drug targets. T… view at source ↗

Figures from the paper (8 more)

**Figure 4.** Figure 4: illustrates an advanced CNN system integrating CT and histology images, employing three CNN modules and ASPP for multi-scale contextual feature extraction. After global average pooling, the unified vector is fed into a pre-trained Dense Net and classification block to …

**Figure 5.** Figure 5: AI significantly enhances lung cancer management by improving early detection, diagnosis, and personalized treatment plans. It aids in survival prediction and optimizes immunotherapy by analyzing patientspecific data and immune responses. The graphic demonstrates a fu…

**Figure 6.** Figure 6: The diagram shows how AI and machine learning methods spanning supervised, unsupervised, semisupervised, and deep learning enhance breast cancer management through early detection, precise diagnosis, personalized treatment, and improved survival outcomes [PITH_FULL_I…

**Figure 9.** Figure 9: This image illustrates the role of AI in gastric cancer management, highlighting its applications in diagnostic imaging, personalized treatment, and targeted drug delivery. AI aids in analyzing tumor characteristics, optimizing treatment plans, and enhancing medication…

**Figure 10.** Figure 10: The diagram shows how to use CNNs, to identify ESCC from endoscopic images. In order to get set up for examination, the incoming data first undergoes preprocessing, which includes normalization. After that, the data is processed through two different CNN modules, each…

**Figure 11.** Figure 11: Artificial Intelligence Workflow in Prostate Cancer Diagnosis and Management The image highlights the significant importance of technology, specifically AI, in the treatment of prostate cancer. The initial diagnostic inputs consist of computer analytics, radiological …

**Figure 12.** Figure 12: The image delivers a detailed and complete structure for the detection and classification of thyroid cancer employing AI, explaining each step from image acquisition to diagnosis. It emphasizes the use of radiological imaging modalities, pre-processing, and deep learn…

**Figure 14.** Figure 14: This figure, emphasizing on the use of AI in the detection and treatment of skin cancer, divides different skin disorders into three categories: benign, malignant, and non-neoplastic. Inflammatory and blistering diseases are examples of non-neoplastic disorders; benig…

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Forward citations

Cited by 1 Pith paper

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Benchmarking Quantum and Classical Machine Learning Models on Oncological Data
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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

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